{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "becoming-being",
   "metadata": {},
   "source": [
    "# Modelling outliers and special events with dummy variables\n",
    "\n",
    "[Feature Engineering for Time Series Forecasting](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n",
    "\n",
    "In this notebook we show how modelling an outlier or special event can improve a forecast.\n",
    "\n",
    "We will work with a monthly retail sales dataset (found [here](https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv)).\n",
    "\n",
    "For instructions on how to download, prepare, and store the dataset, refer to notebook number 1, in the folder \"01-Datasets\" from this repo.\n",
    "\n",
    "## Data Set Synopsis\n",
    "\n",
    "The timeseries is between January 1992 and Apr 2005.\n",
    "\n",
    "It consists of a single series of monthly values representing sales volumes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "mental-presence",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"talk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "precise-combat",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "beginning-seller",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load retail sales dataset with the artificially added outliers\n",
    "df = pd.read_csv(\n",
    "    \"../Datasets/example_retail_sales_with_outliers.csv\",\n",
    "    parse_dates=[\"ds\"],\n",
    "    index_col=[\"ds\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mediterranean-record",
   "metadata": {},
   "source": [
    "# Plot the data to visually inspect any outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "personal-runner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "df.plot(y=\"y\", marker=\".\", figsize=[10, 5], legend=None, ax=ax)\n",
    "ax.set_title(\"Retail Sales with outliers\")\n",
    "ax.set_ylabel(\"Retail Sales\")\n",
    "ax.set_xlabel(\"Time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3efe414e-1a1f-4e80-8420-c4884be14ff2",
   "metadata": {},
   "source": [
    "For this exercise, assume we know the cause of these outliers and therefore we know the dates in the past and future. We're just trying to see the impact of outliers on a model in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "56efc458-3e44-4a16-bdcd-4d8846d1e45d",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_dates = [datetime.date(1993, 9, 1),\n",
    "                 datetime.date(1994, 10, 1),\n",
    "                 datetime.date(1997, 7, 1),\n",
    "                 datetime.date(2004, 7, 1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "second-repeat",
   "metadata": {},
   "source": [
    "# Create dummy variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "05f78c94-eefe-420e-864b-e230d7869429",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"is_outlier\"] = np.where(df.index.isin(outlier_dates), 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf6708b-5ecb-4347-9061-9b4df4053650",
   "metadata": {},
   "source": [
    "Let's check the dummy variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a2f280b-8a28-4ed0-9030-1a0c614c1baf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>is_outlier</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993-01-01</th>\n",
       "      <td>153221.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-02-01</th>\n",
       "      <td>150087.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-03-01</th>\n",
       "      <td>170439.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-04-01</th>\n",
       "      <td>176456.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-05-01</th>\n",
       "      <td>182231.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-06-01</th>\n",
       "      <td>181535.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-07-01</th>\n",
       "      <td>183682.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-08-01</th>\n",
       "      <td>183318.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-09-01</th>\n",
       "      <td>301590.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-10-01</th>\n",
       "      <td>182737.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-01</th>\n",
       "      <td>187443.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-01</th>\n",
       "      <td>224540.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y  is_outlier\n",
       "ds                              \n",
       "1993-01-01  153221.0           0\n",
       "1993-02-01  150087.0           0\n",
       "1993-03-01  170439.0           0\n",
       "1993-04-01  176456.0           0\n",
       "1993-05-01  182231.0           0\n",
       "1993-06-01  181535.0           0\n",
       "1993-07-01  183682.0           0\n",
       "1993-08-01  183318.0           0\n",
       "1993-09-01  301590.2           1\n",
       "1993-10-01  182737.0           0\n",
       "1993-11-01  187443.0           0\n",
       "1993-12-01  224540.0           0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"1993\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889864b5-18e0-4f41-a57e-b5091a74d703",
   "metadata": {},
   "source": [
    "# Add a trend feature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a283893-6db5-40ff-a759-24dc92057365",
   "metadata": {},
   "source": [
    "We will now add a feature to just model the trend. We will go into trend features in far more detail in the Trend and Seasonality section of the course. Our focus is on how outliers can impact our forecasts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27f9901-3474-4e5d-85f1-8476edc2e818",
   "metadata": {},
   "source": [
    "We can model the trend with a feature that represents time $t$ that has passed since any arbitrary time of our choice, we choose the start our time series. We are working with monthly data so we will measure time in months."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1da1fae-33b9-4509-8e7f-4b055dbc8f19",
   "metadata": {},
   "source": [
    "As the time series has no gaps, this can easily be done as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6acfe006-cef4-4e57-8056-5cb7206005a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"trend\"] = np.arange(0, len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47ff452a-401c-4d30-9b59-3ca7d7680f84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>is_outlier</th>\n",
       "      <th>trend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>146376.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>147079.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>159336.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>163669.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>170068.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-01</th>\n",
       "      <td>387155.0</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>293261.0</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>295062.0</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>339141.0</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>335632.0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y  is_outlier  trend\n",
       "ds                                     \n",
       "1992-01-01  146376.0           0      0\n",
       "1992-02-01  147079.0           0      1\n",
       "1992-03-01  159336.0           0      2\n",
       "1992-04-01  163669.0           0      3\n",
       "1992-05-01  170068.0           0      4\n",
       "...              ...         ...    ...\n",
       "2004-12-01  387155.0           0    155\n",
       "2005-01-01  293261.0           0    156\n",
       "2005-02-01  295062.0           0    157\n",
       "2005-03-01  339141.0           0    158\n",
       "2005-04-01  335632.0           0    159\n",
       "\n",
       "[160 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5edc2017-0800-43a1-92f0-b56bab4abbc2",
   "metadata": {},
   "source": [
    "# Split into train and test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ae7f22-94be-43de-b4e7-6d3c6e36036d",
   "metadata": {},
   "source": [
    "We will train the data up to the end of 2003 and predict all the data after that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb7b5183-11b1-4150-9abd-9e281fb9ef75",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df[df.index < \"2004-01-01\"] \n",
    "df_test =df[df.index >= \"2004-01-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fa0ee86-068b-467c-8ad6-2330c3ce323e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x132233880>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "df_train[\"y\"].plot(label=\"train\", ax=ax)\n",
    "df_test[\"y\"].plot(label=\"test\", ax=ax)\n",
    "ax.set_title(\"Retail Sales with outliers\")\n",
    "ax.set_ylabel(\"Retail Sales\")\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eae6130-3bf3-44e8-b432-227a91f65b18",
   "metadata": {},
   "source": [
    "# Train a model with just trend and no dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b9b96ef-0b57-45e0-b35f-47fd7589d522",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d289a65c-c8a5-4a03-8e2a-df770a0e81dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"trend\"]\n",
    "\n",
    "X_train = df_train[features]\n",
    "y_train = df_train[\"y\"].to_frame()\n",
    "\n",
    "X_test = df_test[features]\n",
    "y_test = df_test[\"y\"].to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d03e18a-6754-40d5-a092-4a4a2207f1e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            trend\n",
       "ds               \n",
       "1992-01-01      0\n",
       "1992-02-01      1\n",
       "1992-03-01      2\n",
       "1992-04-01      3\n",
       "1992-05-01      4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>146376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>147079.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>159336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>163669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>170068.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y\n",
       "ds                  \n",
       "1992-01-01  146376.0\n",
       "1992-02-01  147079.0\n",
       "1992-03-01  159336.0\n",
       "1992-04-01  163669.0\n",
       "1992-05-01  170068.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(X_train.head())\n",
    "display(y_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e90ba356-3545-4028-ad06-210315f2ba80",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lf/8j5fhbgs6w91njx87pb8mk2w0000gq/T/ipykernel_53880/2175678095.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_test[\"y_pred_no_dummy\"] = model.predict(X_test)\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "df_test[\"y_pred_no_dummy\"] = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79cad318-23b4-401d-bd58-c332fe27194d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x133029e50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "df_train[\"y\"].plot(label=\"train\", ax=ax)\n",
    "df_test[\"y\"].plot(label=\"test\", ax=ax)\n",
    "df_test[\"y_pred_no_dummy\"].plot(label=\"forecast no dummy\", ax=ax)\n",
    "ax.set_title(\"Retail Sales with outliers\")\n",
    "ax.set_ylabel(\"Retail Sales\")\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58ce03b-e754-44d8-a65b-f942358b7149",
   "metadata": {},
   "source": [
    "# Train a model with trend and dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ed6616fa-fd2c-4271-bad6-030f31fc88d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"trend\", \"is_outlier\"]\n",
    "\n",
    "X_train = df_train[features]\n",
    "y_train = df_train[\"y\"].to_frame()\n",
    "\n",
    "X_test = df_test[features]\n",
    "y_test = df_test[\"y\"].to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f791ecdd-c826-47d3-a034-5942463cd9a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trend</th>\n",
       "      <th>is_outlier</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            trend  is_outlier\n",
       "ds                           \n",
       "1992-01-01      0           0\n",
       "1992-02-01      1           0\n",
       "1992-03-01      2           0\n",
       "1992-04-01      3           0\n",
       "1992-05-01      4           0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>146376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>147079.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>159336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>163669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>170068.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y\n",
       "ds                  \n",
       "1992-01-01  146376.0\n",
       "1992-02-01  147079.0\n",
       "1992-03-01  159336.0\n",
       "1992-04-01  163669.0\n",
       "1992-05-01  170068.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(X_train.head())\n",
    "display(y_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da6cd739-509e-454f-8585-0a261eb70e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lf/8j5fhbgs6w91njx87pb8mk2w0000gq/T/ipykernel_53880/2284433834.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_test[\"y_pred_w_dummy\"] = model.predict(X_test)\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "df_test[\"y_pred_w_dummy\"] = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "90fb3d5f-5df3-40ab-b687-7eddd38e5b37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x133007880>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=[10, 5])\n",
    "df_train[\"y\"].plot(label=\"train\", ax=ax)\n",
    "df_test[\"y\"].plot(label=\"test\", ax=ax)\n",
    "df_test[\"y_pred_w_dummy\"].plot(label=\"forecast with dummy\", ax=ax)\n",
    "ax.set_title(\"Retail Sales with outliers\")\n",
    "ax.set_ylabel(\"Retail Sales\")\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f523e04-011a-4ae6-8974-9b055513e152",
   "metadata": {},
   "source": [
    "We can see that adding a dummy variable allows us to estimate the impact of future outlier or special events. This is useful when we know in advance when such events will occur."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
